The emergence of the data science profession

Philipp Soeren Brandt
2017
This thesis studies the formation of a novel expert role—the data scientist—in order to ask how arcane knowledge becomes publicly salient. This question responds to the two-sided public debate, wherein data science is associated with problems such as discriminatory consequences and privacy infringements, but has also become linked with opportunities related to new forms of work. A puzzle arises also, as institutional boundaries have obscured earlier instances of quantitative expertise. Even a
more » ... oader perspective reveals few expert groups that have gained lay salience on the basis of arcane knowledge, other than lawyers and doctors. This empirical puzzle recovers a gap in the literature between two main lines of argument. An institutionalist view has developed ways for understanding expert work with respect to formal features such as licensing, associations and training. A constructivist view identifies limitations in those arguments, highlighting their failure to explain many instances in which arcane knowledge emerges through informal processes, including the integration of lay knowledge through direct collaboration. Consistent with this critique, data nerds largely define their work on an informal basis. Yet, they also draw heavily on a formalized stock of knowledge. In order to reconcile the two sides, this thesis proposes viewing data science as an emerging "thought community." Such a perspective leads to an analytical strategy that scrutinizes contours that emerge as data nerds define arcane expertise as theirs. The analysis unfolds across three empirical settings that complement each other. The first setting considers data nerds as they define their expertise in the context of public events in New York City's technology scene. This part draws on observations beginning in 2012, shortly after data science's first lay recognition, and covers three years of its early emergence. Two further studies comparatively test whether and in what ways contours of data science's abstract knowledge are associate [...]
doi:10.7916/d8bk1ckj fatcat:xzraspk2tfbkppfcadrjpguvcu